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On Efficiently Acquiring Annotations for Multilingual Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AWT4LPUIU" target="_blank" >RIV/00216208:11320/22:WT4LPUIU - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.acl-short.9" target="_blank" >https://aclanthology.org/2022.acl-short.9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2022.acl-short.9" target="_blank" >10.18653/v1/2022.acl-short.9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On Efficiently Acquiring Annotations for Multilingual Models

  • Original language description

    When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages. In this work, we show that the strategy of joint learning across multiple languages using a single model performs substantially better than the aforementioned alternatives. We also demonstrate that active learning provides additional, complementary benefits. We show that this simple approach enables the model to be data efficient by allowing it to arbitrate its annotation budget to query languages it is less certain on. We illustrate the effectiveness of our proposed method on a diverse set of tasks: a classification task with 4 languages, a sequence tagging task with 4 languages and a dependency parsing task with 5 languages. Our proposed method, whilst simple, substantially outperforms the other viable alternatives for building a model in a multilingual setting under constrained budgets.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

  • ISBN

    978-1-955917-22-3

  • ISSN

  • e-ISSN

  • Number of pages

    17

  • Pages from-to

    69-85

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

  • Event location

    Dublin, Ireland

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article